Historically, semiconductor device manufacturers have managed the transition to tighter process/materials specifications by depending on process tool manufacturers to design better and faster process/hardware configurations. As device geometries shrink to the nanometer scale, however, the increasing complexity of manufacturing processes has changed the landscape that must be negotiated to meet and maintain process/materials specifications.
A typical process tool used today in semiconductor manufacturing is described by a set of several thousand variables. In some cases, of these several thousand variables, there will be several hundred relevant dynamic variables. The dynamic variables (e.g., gas flow, gas pressure, delivered power, current, voltage) change based on, for example, the specific processing recipe, the step in the overall set of processing steps, or errors or faults occurring in the manufacturing process.
By way of example, if a given semi wafer manufacturing process has 200 dynamic variables that are each sampled by a data acquisition system at a rate of one sample per second (or faster) and a wafer requires 30 seconds to process, the data acquisition system will acquire 6000 data points (or more). It is quite difficult for an operator to look at the raw data traces plotted on a screen for each of the 200 variables to determine if, for example, the process is progressing properly or if a fault has occurred that would cause a defect in a specific wafer.
Meaningful application of this potential flood of data in process control is a formidable task. Simpler approaches, such as univariate statistical process control (USPC), are well established, but have limitations. USPC is effective in the observation and control of a single response parameter but advanced device fabrication requires control of multiple manufacturing variables simultaneously. Manufacturing variables typically have complex interrelationships that USPC can neither evaluate nor control.
A need therefore exists for improved systems and methods for identifying states of a manufacturing process and for controlling the manufacturing process.